Life

BackgroundNETosis is a key innate immune defence mechanism where neutrophils release extracellular traps (NETs). However, excessive NET formation may damage organs during sepsis. We investigated the association between NETs and sepsis outcomes, including mortality and acute kidney injury (AKI). MethodsWe analysed levels of H3.1 nucleosomes in 971 patients with severe sepsis and septic shock from the SISPCT trial (Effect of Sodium Selenite Administration and Procalcitonin-Guided Therapy on Mortality in Patients With Severe Sepsis or Septic Shock). We evaluated associations between H3.1 levels and mortality and the need for renal replacement therapy using multivariable Cox regression and receiver operating characteristic analyses. ResultsWe analysed 971 critically ill patients with complete data including admission H3.1 levels. 443 patients (45.6%) presented with sepsis, 520 (53.6%) had septic shock, and eight patients had an unknown diagnosis as defined by Sepsis-3. Admission H3.1 levels were higher in patients with septic shock than with sepsis (median 921.84 vs 432.71 ng/mL; p<0.001). Admission H3.1 levels were higher in non-survivors, and in a univariate Cox analysis, each log-10 increase in H3.1 was associated with a hazard ratio of 1.86 (95% confidence interval 1.41-2.47, p<0.05). H3.1 was also higher in patients requiring renal replacement therapy with septic shock vs sepsis (1832 ng/mL vs 801.4 ng/mL, p=0.01) and demonstrated a dose-response relationship with the severity of AKI. ConclusionElevated levels of H3.1 nucleosomes at admission are independently associated with mortality and severe kidney dysfunction requiring renal replacement therapy. Trial registrationClinicaltrials.gov Identifier, NCT00832039.

ImportanceThe outbreak of highly contagious COVID-19 has posed a serious threat to human health, especially for those with underlying diseases. However, Impacts of COVID-19 epidemic on HD center and HD patients have not been reported. ObjectiveTo summery an outbreak of COVID-19 epidemic in HD center. Design, Setting, and ParticipantsWe reviewed the epidemic course from the first laboratory-confirmed case of COVID-19 infection on January 14 to the control of the epidemic on March 12 in the HD center of Renmin Hospital of Wuhan University. Total 230 HD patients and 33 medical staff were included in this study ExposuresCOVID-19. Main Outcomes and MeasuresEpidemiological, clinical, laboratory, and radiological characteristics and outcomes data were collected and analyzed. 19 COVID-19 HD patients, 19 non-COVID-19 HD patients and 19 healthy volunteers were enrolled for further study about the effect of SARS-CoV-2 infection on host immune responses. Results42 out of 230 HD patients (18.26%) and 4 out of 33 medical staffs (12.12%) were diagnosed with COVID-19 from the outbreak to March 12, 2020. 13 HD patients (5.65%), including 10 COVID-19 diagnosed, died during the epidemic. Only 2 deaths of the COVID-19 HD patients were associated with pneumonia/lung failure. Except 3 patients were admitted to ICU for severe condition (8.11%), including 2 dead, most COVID-19 diagnosed patients presented mild or none-respiratory symptoms. Multiple lymphocyte populations in HD patients were significantly decreased. HD patients with COVID-19 even displayed more remarkable reduction of serum inflammatory cytokines than other COVID-19 patients. Conclusions and RelevanceHD patients are the highly susceptible population and HD centers are high risk area during the outbreak of COVID-19 epidemic. HD Patients with COVID-19 are mostly clinical mild and unlikely progress to severe pneumonia due to the impaired cellular immune function and incapability of mounting cytokines storm. More attention should be paid to prevent cardiovascular events, which may be the collateral impacts of COVID-19 epidemic on HD patients.

Ionizing radiation is one of the main threats to human space exploration beyond low Earth orbit (BLEO). It is thus of primary importance to determine safe career dose limits for astronauts involved in BLEO missions. In the first part of this work it is shown how the methods of physics and statistics can contribute to its solution. The average equivalent doses received by a hypothetical human crew are established using the data of several robotic missions to the Moon and to Mars. The probabilities of the occurrence of deterministic effects due to radiation that could impair the success of a mission or lower the life expectancy of astronauts are evaluated with the help of a statistical analysis. In the last part of this work it is argued that the use of the so-called 3D models or organoids combined with the methods of precision oncology and molecular medicine could be a good candidate of a strategy in order to predict the insurgence of stochastic effects in humans. On one side, organoids recapitulate several features of the real human organs and their in vivo surroundings. On the other side, we argue with a case study that precision oncology and molecular medicine are able to provide a deeper insight of the onset of cancer following irradiation in space.

BackgroundGlioblastoma multiforme (GBM) is the most aggressive and lethal form of primary brain tumor, characterized by rapid growth and resistance to conventional therapies. Despite advances in treatment, most patients succumb to the disease within 15 months. Drug repurposing, which involves finding new uses for existing drugs, is a promising strategy to develop new GBM treatments faster and more cost-effectively. MethodWe obtained single-cell RNA sequencing (scRNA-seq) data (GSE84465) from the National Institutes of Health (NIH) Gene Expression Omnibus (GEO) repository to compare gene expression in GBM neoplastic cells and non-neoplastic cells. We identified genes that were abnormally expressed in tumor cells and linked these genes to potential drug targets. To identify potential repurposed drugs for GBM, we leveraged the Chemical Entities of Biological Interest (ChEBI) database to assess the interaction of various compounds with the differentially expressed genes identified in the scRNA-seq analysis. We focused on compounds that could reverse the aberrant gene expression observed in GBM neoplastic cells. ResultsOur analysis suggests that ivermectin and all-trans-retinoic acid (ATRA) could be repurposed as effective treatments for GBM. Ivermectin, typically used as an antiparasitic, demonstrated strong anti-tumor activity by downregulating 40 of the top 100 upregulated genes in GBM, indicating its potential to suppress tumor growth. ATRA, known for promoting cell differentiation, upregulated 60 genes typically downregulated in GBM neoplastic cells, showing its potential to correct transcriptional dysregulation and support tumor suppression. These findings underscore the promise of drug repurposing to target key pathways in GBM, offering new therapeutic options for this aggressive cancer. ConclusionsOur results provide compelling evidence that ivermectin and ATRA may be effective in treating GBM. The observed alterations in gene expression indicate the ability of these two agents to disrupt key genes and pathways crucial for tumor progression. Given the increasing interest in drug repurposing for cancer treatment, comprehensive preclinical and clinical investigations are warranted to assess fully the therapeutic efficacy of these compounds against this disease.

IntroductionTwo waves of COVID-19 cases have overwhelmed most European countries during 2020. It is unclear if the incidence of acute kidney injury (AKI) has changed during the COVID-19 outbreaks. This study aims to evaluate the differences in incidence, risk factors and outcome of AKI in patients with SARS-CoV-2 infection during the first and second wave of COVID-19. MethodWe reviewed the health medical records of 792 consecutive patients with COVID-19 hospitalized at the University Hospital of Modena, Italy, from February 25 to December 14, 2020. ResultsAKI was diagnosed in 122 (15.4%) patients. Incidence of AKI remained steady rate during wave-1 (15.9%) and wave-2 (14.7%) (P=0.89). AKI patients were older (P=<0.001) and had a more severe respiratory impairment (PO2/FO2) (P=[&le;]0.001) than their non-AKI counterparts. AKI led to a longer hospital stay (P=0.001), complicated with a higher rate of ICU admission. COVID-19-related AKI was associate with 59.7% of deaths during wave-1 and 70.6% during wave-2. At the end of the period of observation, 24% (wave-1) and 46.7% (wave-2) of survivors were discharged with a not fully recovered kidney function. Risk factors for AKI in patients with COVID-19 were diuretics (HR=5.3; 95%CI, 1.2-23.3; P=0.025) and cardiovascular disease (HR, 2.23; 95%CI, 1.05-5.1; P=0.036). ConclusionThe incidence of AKI (about 15%) remained unchanged during 2020, regardless of the trend of COVID-19. AKI occurred in patients with severe COVID-19 symptoms and was associated with a higher incidence of deaths than non-AKI patients. The risk factors of COVID-19-related AKI were diuretic therapy and cardiovascular disease.

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic globally changed respiratory infection patterns. However, its impact on community-acquired pneumonia (CAP) in high risk patients with haematological malignancies (HM) is uncertain. We aimed to examine CAP aetiology changes in patients with HM pre- and post-COVID-19 pandemic. MethodsThis retrospective study included 524 HM patients hospitalised with CAP between March 2018 and February 2022. Those who underwent bronchoscopy within 24 hours after admission to identify CAP aetiology were included. Data on patient characteristics, laboratory findings, and results of bronchioalveolar lavage fluid cultures and PCR tests were analysed to compare etiological changes and identify in-hospital mortality risk factors. ResultsPatients were divided into pre-COVID-19 (44.5%) and post-COVID-19 (55.5%) groups. This study found a significant decrease in viral CAP in the post-COVID-19 era, particularly for influenza A, parainfluenza, adenovirus, and rhinovirus (3.0% vs. 0.3%, respectively, P = 0.036; 6.5% vs. 0.7%, respectively, P = 0.001; 5.6% vs. 1.4%, respectively, P = 0.015; 9.5% vs. 1.7%, respectively, P < 0.001). Bacterial, fungal, and unknown CAP aetiologies remain unchanged. Higher Sequential Organ Failure Assessment scores and lower platelet count correlated with in-hospital mortality after adjusting for potential confounding factors. ConclusionThe incidence of CAP in HM patients did not decrease after COVID-19. Additionally, CAP aetiology among patients with HM changed following the COVID-19 pandemic, with a significant reduction in viral pneumonia while bacterial and fungal pneumonia persisted. Further studies are required to evaluate the impact of COVID-19 on the prognosis of patients with HM and CAP.

ObjectiveCOVID-19 pandemic is a danger for the whole world. Also, our knowledge about acute kidney injury (AKI) in COVID-19 patients is incomplete. Few studies informed that the problem of AKI is a common complication, but other studies concluded that AKI is only an unusual event during COVID-19 infection. This study using meta-analysis tools aimed to find disease progression and mortality risk in affected population. MethodsWe systematically reviewed the literature on COVID-19 and its association with AKI as per PRISMA guideline. All authors independently performed a literature search until 8th June 2023. We included studies which reported clinical characteristics, incidence of AKI, and the death risk with AKI during COVID-19 infection. FindingsWe have included five studies and all of them reported older age (73-75) and males (67-84.2%) were risk factors for patient illness. COVID-19 patients with AKI had more than five times mortality risk of those without AKI. Diagnosis time after disease onset was 8.5 days (IQR, [4-11]). Fatality time after initial hospital admission was 13.5 days (IQR, 8-17). In non-survivors, systemic inflammation with high temperature, abnormal respiratory rate, acute myocardial injury, and acute respiratory distress syndrome (ARDS) were observed. Abnormal biochemical analytes and immunological markers were observed. ConclusionOur analyses indicate that patients experienced repeated changes in biochemical analytes and immune marker with the progression of the disease. It indicates the requirement of early management and treatment. Further study is required to conclude and to have better knowledge of AKI mechanism with COVID-19 infection.

Synthetic biology applications are currently based on the programming of bacteria with tailor-made circuits designed ad hoc by applying a top-down strategy. We introduce a novel algorithm oriented to design synthetic bacteria according to a bottom-up approach, i.e. via an evolutionary programming algorithm. The proposed algorithm has been referred to as GADY: an acronym for Get signal, Antidote, Die when a killer gene is expressed, emits Yellow fluorescence. We include in this technical note a script of the algorithm in Gro cellular programming language, programming a colony of synthetic bacteria and illustrating its usefulness in a case of bioremediation or elimination of a strain of pathogenic bacteria.

In their paper Natural selection for least action (Kaila and Annila 2008) they depict evolution as a process conforming to the Principle of Least Action (PLA). From this concept, together with the Coevolution model of Lewontin, an equation of motion for environmental coevolution is derived which shows that it is the time rate (frequency) of evolutionary change of the organism (mutations) that responds to changes in the environment. It is not possible to compare the theory with viral or bacterial mutation rates, as these are not measured on a time base. There is positive evidence from population level avian studies where the coefficient of additive evolvability (Cav) and its square (IA) change with environmental favourability in agreement with this model. Further analysis shows that the time rate of change of the coefficient of additive evolvability (Cav) and its square (IA) are linear with environmental favourability, which could help in defining the Lagrangian of the environmental effects.

Mosses display resilience and ecological importance, significantly shaping their environments. With their strong attachment to challenging substrates, mosses can serve as viable options for green living facades. In our initial steps towards developing sensing and computing living facades using moss, we analysed the endogenous electrical activity of mosses to establish foun-dational knowledge for future information processing devices. Employing macro-electrode recording techniques, we identified three patterns of electrical activity in ordinary moss: high-frequency oscillations at 1.2 Hz, medium-frequency oscillations at 2 {middle dot} 10-4 Hz, and low-frequency oscillations at approximately 4 {middle dot} 10-4. Additionally, we observed indications of coordinated electrical activity in moss cushions.

Human infection with the SARS-CoV-2 virus leads to coronavirus disease (COVID-19). A striking characteristic of COVID-19 infection in humans is the highly variable host response and the diverse clinical outcomes, ranging from clinically asymptomatic to severe immune reactions leading to hospitalization and death. Here we used a 3D genomic approach to analyse blood samples at the time of COVID diagnosis, from a global cohort of 80 COVID-19 patients, with different degrees of clinical disease outcomes. Using 3D whole genome EpiSwitch(R) arrays to generate over 1 million data points per patient, we identified a distinct and measurable set of differences in genomic organization at immune-related loci that demonstrated prognostic power at baseline to stratify patients with mild forms of illness and those with severe forms that required hospitalization and intensive care unit (ICU) support. Further analysis revealed both well established and new COVID-related dysregulated pathways and loci, including innate and adaptive immunity; ACE2; olfactory, G{beta}{psi}, Ca2+ and nitric oxide (NO) signalling; prostaglandin E2 (PGE2), the acute inflammatory cytokine CCL3, and the T-cell derived chemotactic cytokine CCL5. We identified potential therapeutic agents for mitigation of severe disease outcome, with several already being tested independently, including mTOR inhibitors (rapamycin and tacrolimus) and general immunosuppressants (dexamethasone and hydrocortisone). Machine learning algorithms based on established EpiSwitch(R) methodology further identified a subset of 3D genomic changes that could be used as prognostic molecular biomarker leads for the development of a COVID-19 disease severity test.

BackgroundThere are many small datasets of significant value in the medical space that are being underutilized. Due to the heterogeneity of complex disorders found in oncology, systems capable of discovering patient subpopulations while elucidating etiologies is of great value as it can indicate leads for innovative drug discovery and development. Materials and MethodsHere, we report on a machine intelligence-based study that utilized a combination of two small non-small cell lung cancer (NSCLC) datasets consisting of 58 samples of adenocarcinoma (ADC) and squamous cell carcinoma (SCC) and 45 samples (GSE18842). Utilizing a set of standard machine learning (ML) methods which are described in this paper, we were able to uncover subpopulations of ADC and SCC while simultaneously extracting which genes, in combination, were significantly involved in defining the subpopulations. We also utilized a proprietary interactive hypothesis-generating method designed to work with machine learning methods, which provided us with an alternative way of pinpointing the most important combination of variables. The discovered gene expression variables were used to train ML models. This allowed us to create methods using standard methods and to also validate our in-house methods for heterogeneous patient populations, as is often found in oncology. ResultsUsing these methods, we were able to uncover genes implicated by other methods and accurately discover known subpopulations without being asked, such as different levels of aggressiveness within the SCC and ADC subtypes. Furthermore, PIGX was a novel gene implicated in this study that warrants further study due to its role in breast cancer proliferation. ConclusionHere we demonstrate the ability to learn from small datasets and reveal well-established properties of NSCLC. This demonstrates the utility for machine learning techniques to reveal potential genes of interest, even from small data sets, and thus the driving factors behind subpopulations of patients.

The incidence of acute kidney injury (AKI) in hospitalized patients with coronavirus disease 2019 (COVID-19) is variable, being associated with worse outcomes. The objectives of the study were to evaluate the incidence, risk factors and impact of AKI in subjects hospitalized for COVID-19 in two third- level hospitals in Cordoba, Argentina. A retrospective cohort study was conducted. 448 adults who were consecutively hospitalized for COVID-19 between March and the end of October 2020 at Hospital Privado Universitario de Cordoba and Hospital Raul Angel Ferreyra were included. The incidence of AKI was 19% (n = 85). 50.6% presented AKI stage 1 (n=43), 20% stage 2 (n=17) and 29.4% stage 3 (n=25, of which 18 required renal replacement therapy). In the multivariate analysis, the variables that were independently associated with AKI were: age (adjusted Odd ratio -aOR- =1.30, 95%CI=1.04-1.63, p=0.022), history of chronic kidney disease (aOR=9.92, 95%CI=4.52-21.77, p<0.001), blood neutrophil count at admission (aOR=1.09, 95%CI=1.01-1.18, p=0.037) and requirement for mechanical ventilation (MV) (aOR=6.69, 95%CI=2.24-19.9, p=0.001). AKI was associated with longer hospitalization, greater admission and length of stay in the intensive care unit, a positive association with bacterial superinfection, sepsis, respiratory distress syndrome, MV requirement and mortality (mortality with AKI=47.1% vs without AKI=12.4%, p<0.001). AKI was independently associated with higher mortality (aOR=3.32, 95%CI=1.6-6.9, p=0.001). In conclusion, the incidence of AKI in adults hospitalized for COVID-19 was 19% and had a clear impact on morbidity and mortality. Local predisposing factors for AKI were identified.

BackgroundAcute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy. Although induction therapy induces remission in many patients, relapse and acquired chemoresistance remain the major causes of treatment failure. Defining the molecular mechanisms underlying relapse is essential for improving therapeutic strategies. MethodsBone marrow samples from nine AML patients were analyzed, including five newly diagnosed cases and four relapsed cases after chemotherapy. Transcriptome sequencing and integrative bioinformatics analyses were performed, including differential expression analysis, GO/KEGG enrichment, GSEA, and protein-protein interaction (PPI) network analysis, to delineate relapse-associated molecular alterations. ResultsPrincipal component analysis demonstrated clear transcriptional segregation between primary and relapsed AML, indicating extensive molecular reprogramming during relapse. A total of 2,025 differentially expressed genes were identified, enriched in pathways related to epithelial-mesenchymal transition-like programs, leukemia stem cell maintenance, apoptosis evasion, and bone marrow microenvironment remodeling. Marked upregulation of FOXC1, HOXA11/HOXA11-AS, and AXL suggests key roles in sustaining stemness and promoting drug resistance. GO/KEGG analysis revealed coordinated activation of small GTPase, Rho/Ras signaling, ion transport, and epigenetic regulatory pathways, reflecting multilayered adaptive responses. GSEA indicated metabolic-epigenetic reprogramming in relapsed AML, while primary AML showed enrichment of energy metabolism and chromatin assembly pathways. PPI network analysis highlighted a central inflammation-metabolism axis involving TP53, IL6, CXCL8, and CCL2, associated with apoptosis resistance and metabolic adaptation. ConclusionsRelapsed AML is characterized by transcriptional reprogramming, metabolic remodeling, and microenvironment-driven adaptive resistance. Targeting small GTPase signaling, AXL, or IL6-related inflammatory pathways, alone or combined with epigenetic modulators, may offer promising therapeutic strategies to overcome chemoresistance in AML.

BackgroundKidney transplant recipients with COVID-19 are at high risk of poor outcome because of comorbidities and immunosuppression. The effects of immunosuppressive therapy reduction are unclear in patients with COVID-19. MethodsWe conducted a retrospective study on 45 consecutive kidney transplant recipients followed at the University Hospital of Modena who tested positive for COVID-19 by RT-PCR analysis. ResultsThe median age of patients was 56.1 (interquartile range, [IQR] 47.3-61.1) years with a predominance of male (64.4%). Kidney transplantation vintage was 10.1 (2.7-16) years, and more than half of patients (55.6%) was on triple immunosuppressive therapy. Early reduction of immunosuppression occurred in 62.8% of patients and included antimetabolite (88.8%) and calcineurin inhibitor withdrawal (22.2%). Of the 45 patients, 88.9% became symptomatic and 40% required hospitalization. Overall mortality was 17.8%. There were no differences in outcomes between full- and reduced-dose immunosuppressive therapy at the end of follow-up. One hospitalized patient experienced irreversible graft failure. There were no differences in serum creatinine level and proteinuria in non-hospitalized patients with COVID-19. Admitted patients had better kidney function after dismission (P=0.019). Risk factors for death were age (odds ratio [OR]: 1.19; 95%CI: 1.01-1.39), and duration of kidney transplant (OR: 1.17; 95%CI: 1.01-1.35). One kidney transplant recipient experienced symptomatic COVID-19 reinfection after primary infection and anti-SARS-CoV-2 mRNA vaccine. ConclusionsDespite the reduction of immunosuppression, COVID-19 affected survival of kidney transplant recipients with COVID-19. Age and duration of kidney transplant were independent predictors of death in COVID-19. Early kidney function was favorable in most survivors after COVID-19.

BackgroundMonoclonal B lymphocytosis (MBL) increases with age and individuals with high count MBL progress to chronic lymphocytic leukaemia requiring therapy at a rate of [~]1%-5% per year. These cases usually have atypical lymphocytes at the microscope, abnormal representation in the scattergram, and positivity of flags. Using XN9000 (Sysmex), we noticed cases of MBL without this correlation. We studied customized gates for discovering MBL cases of our interest. MethodsWe considered 212 peripheral blood samples with known phenotypes: 76.7% negative and 23.3% positive for B, T, or NK lymphocytes clones. We created gates studying the XN9000 FCS files in Diva software to identify new areas for better delimiting subpopulations of our interest and calculating sensitivity and specificity. ResultsWe found significant differences between negative and positive groups for Q-flag "Blasts/Abn Lympho?" (B/AL) and LY-X (p <0.05) with lymphocyte counts below 5x109/L. A new gate P1 normalized by P2 (P1n) differentiated between phenotypes much better than Q-flag B/AL with lymphocyte counts [&le;] 5 x109/L. Moreover, cases with MBL CD5 positive had higher medians (p <0.05). ConclusionWe propose a gate P1n as a new Q-flag for lymphocytes count [&le;] 5 x109/L, in order to hypothesize the presence of MBL CD5 positives.

DNA is considered a fundamental component of life, yet it remains vulnerable to damage under extreme conditions, such as ionizing radiation exposure. To better understand this fragility, it becomes important to estimate mutation frequencies under different radiation doses. Furthermore, this approach has potential for future applications, especially in the context of deep space exploration, where astronauts are exposed to higher levels of cosmic radiation. For this purpose, we developed a mathematical model by integrating two existing models, the Monte Carlo Excision Repair (MCER) model and the Whack-a-Mole (WAM) model, both specifically adapted for use in manned space missions. The WAM model is supported with the Monte Carlo simulation to address the lack of human experimental data available in previous studies so that by calculating four key variables related to the human cells defined in the WAM model, potential mutations in astronauts during space exploration were estimated. The results showed small deviations from previous studies, which can be attributed to differences in the type of radiation sources as well as the organisms studied being different from those used in previous studies. With this study, researchers can now better predict mutation frequency during deep space missions by considering the impact of cosmic radiation. This is particularly important in the context of future missions to the Moon and Mars, where cosmic radiation will play an important role in mission planning and risk management.

There is limited information describing features and outcomes of patients requiring hospitalization for COVID19 disease and still no treatments have clearly demonstrated efficacy. Demographics and clinical variables on admission, as well as laboratory markers and therapeutic interventions were extracted from electronic Clinical Records (eCR) in 4712 SARS-CoV2 infected patients attending 4 public Hospitals in Madrid. Patients were stratified according to age and stage of severity. Using multivariate logistic regression analysis, cut-off points that best discriminated mortality were obtained for each of the studied variables. Principal components analysis and a neural network (NN) algorithm were applied. A high mortality incidence associated to age >70, comorbidities (hypertension, neurological disorders and diabetes), altered vitals such as fever, heart rhythm disturbances or elevated systolic blood pressure, and alterations in several laboratory tests. Remarkably, analysis of therapeutic options either taken individually or in combination drew a universal relationship between the use of Cyclosporine A and better outcomes as also a benefit of tocilizumab and/or corticosteroids in critically ill patients. We present a large Spanish population-based study addressing factors influencing survival in current SARS CoV2 pandemic, with particular emphasis on the effectivity of treatments. In addition, we have generated an NN capable of identifying severity predictors of SARS CoV2. A rapid extraction and management of data protocol from eCR and artificial intelligence in-house implementations allowed us to perform almost real time monitoring of the outbreak evolution.

Deciphering the mechanisms underlying progenitor cell differentiation and cell-fate decisions is critical for answering fundamental questions regarding hematopoietic lineage commitment. Here, we redefine the entire spectrum of original hematopoietic progenitor cells (HPCs) using a comprehensive transcriptional atlas that effectively delineates the transitional progenitors. This is the first study to fully distinguish the transitional state along hematopoietic progenitor cell differentiation, reconciling previous controversial definitions of common myeloid progenitors (CMPs), granulocyte-monocyte progenitors (GMPs), and lymphoid-primed multipotent progenitors (LMPPs). Moreover, plasma progenitor cells are identified and defined. Transcription factors associated with key cell-fate decisions are identified at each level of the hematopoietic hierarchy, providing novel insights into the underlying molecular mechanisms. The hematopoietic hierarchy roadmap was reformed that reconciles previous models concerning pathways and branches of hematopoiesis commitment. Initial hematopoietic progenitors are simultaneously primed into megakaryocytic-erythroid, lymphoid, and neutrophilic progenitors during the first differentiation stage of hematopoiesis. During initial progenitor commitment, GATA2, HOPX, and CSF3R determine the co-segregation of the three transitional lineage branches. Two types of lineage-commitment processes occur during hematopoiesis: the megakaryocytic-erythroid lineage commitment process is continuous, while the lymphoid-lineage commitment is stepwise. Collectively, these results raise numerous possibilities for precisely controlling progenitor cell differentiation, facilitating advancements in regenerative medicine and disease treatment. HighlightsO_LIHematopoietic progenitors are redefined using a comprehensive transcriptional atlas. C_LIO_LICell fate decision-related transcription factors are revealed in the hematopoietic hierarchy. C_LIO_LIProgenitor lineage commitment includes continuous and stepwise processes. C_LIO_LIThe initial hematopoietic hierarchy is simultaneously primed into three lineages. C_LI

The human tumor suppressor P53 has so far been shown to inhibit growth in the yeast model as well as in other eukaryotic contexts. Despite a considerable number of sudies involving p53 in bacteria, the question of effects on cell growth and viability has never been explored. In this work, we report similar negative effect on cell viability of the protein expressed in Escherichia coli strain BL21(DE3). This inhibition still needs to be characterized in lights of the distinction between yeast and other organisms with different P53-caused deaths and pathways. Primary tests leaned towards an active p53 in this bacterial context, both under normal and stresseful conditions. Special effects were noticed either with phage infection or antibiotic treatment, using a GST-p53 fusion form. These results open the way for further investigations involving P53 in prokaryote systems.